This work proposes a framework of benchmark functions designed to facilitate the creation of test cases for numerical optimisation techniques. The framework, written in Python 3, is designed to be easy to install, use, and expand. The collection includes some of the most used multi-modal continuous functions present in literature, which can be instantiated using an arbitrary number of dimensions. Meta-information of each benchmark function, like search boundaries and position of known optima, are included and made easily accessible through class methods. Built-in interactive visualisation capabilities, baseline techniques, and rigorous testing protocols complement the features of the framework. The framework can be found here: \url{https://gitlab.com/luca.baronti/python_benchmark_functions

翻译：本研究提出一个基准函数框架，旨在促进数值优化技术测试用例的创建。该框架采用Python 3编写，具有易于安装、使用和扩展的特点。该集合包含文献中常用的多模态连续函数，可通过任意维度进行实例化。每个基准函数的元信息（如搜索边界和已知最优解位置）均已包含，并可通过类方法便捷访问。内置的交互式可视化功能、基线技术及严格的测试协议进一步完善了框架特性。该框架可通过以下链接获取：\url{https://gitlab.com/luca.baronti/python_benchmark_functions